By Topic

Application of Genetic Algorithm-Based Artificial Neural Network in Prediction of Aircraft Engine Wear

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Jiang Xufeng ; Xuzhou Air Force Coll., Xuzhou, China ; Guo Changying ; Zhang Yuan ; Wang Jianbo

The time series prediction model based on neural network can perfectly reflect the trend of development of nonlinear system, but the training speed for neural network is very slow, therefore, it is easily prone to local extremum. So we come up with a learning algorithm combining genetic algorithm and BP algorithm for the training of BP neural network, to realize optimization of network structure. We have built a prediction model for aircraft engine wear based o this type of algorithm. Comparisons have been made between the results from this prediction model and those from multiple linear regression method. The final test results indicate that genetic algorithm-based BP neural network is superior to BP algorithm and multiple linear regression method, bringing about much better forecasting results.

Published in:

Digital Manufacturing and Automation (ICDMA), 2010 International Conference on  (Volume:1 )

Date of Conference:

18-20 Dec. 2010